Abstract Adversarial Machine Learning (AML) discusses the act of attacking and defending Machine Learning (ML) Models, an essential building block of Artificial Intelligence (AI). ML …
Deep Neural Networks (DNNs), from AlexNet to ResNet to ChatGPT, have made revolutionary progress in recent years, and are widely used in various fields. The high …
T Miura, T Shibahara, N Yanai - Proceedings of the 2nd ACM Workshop …, 2024 - dl.acm.org
Explainable AI encourages machine learning applications in the real world, whereas data- free model extraction attacks (DFME), in which an adversary steals a trained machine …
I Lederer, R Mayer, A Rauber - IEEE Transactions on Neural …, 2023 - ieeexplore.ieee.org
The commercial use of machine learning (ML) is spreading; at the same time, ML models are becoming more complex and more expensive to train, which makes intellectual property …
Z Hong, L Shen, T Liu - … of the IEEE/CVF Conference on …, 2024 - openaccess.thecvf.com
Recently non-transferable learning (NTL) was proposed to restrict models' generalization toward the target domain (s) which serves as state-of-the-art solutions for intellectual …
Counterfactual explanations find ways of achieving a favorable model outcome with minimum input perturbation. However, counterfactual explanations can also be exploited to …
The robustness of recommender systems has become a prominent topic within the research community. Numerous adversarial attacks have been proposed, but most of them rely on …
S Akoush, A Paleyes, A Van Looveren… - arXiv preprint arXiv …, 2022 - arxiv.org
Inference is a significant part of ML software infrastructure. Despite the variety of inference frameworks available, the field as a whole can be considered in its early days. This position …
F Suya, A Suri, T Zhang, J Hong… - … IEEE Conference on …, 2024 - ieeexplore.ieee.org
Numerous works study black-box attacks on image classifiers, where adversaries generate adversarial examples against unknown target models without having access to their internal …